# Practical Guide to Fine-Tuning Vision-Language Models Based on LLaMA-Factory: Document Understanding and Chart Parsing

> This article introduces a fine-tuning project for Vision-Language Models (VLM) based on the LLaMA-Factory framework, focusing on document understanding, chart parsing, and visual question answering tasks. The project demonstrates how to use LoRA and full fine-tuning techniques to enhance the performance of VLMs in specific domains, and provides complete architecture design, training workflow, and performance evaluation results.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-05-09T23:07:02.000Z
- 最近活动: 2026-05-09T23:16:55.073Z
- 热度: 0.0
- 关键词: VLM, 视觉语言模型, LLaMA-Factory, LoRA, 文档理解, 图表解析, 多模态AI, 微调, Transformer, 分组查询注意力
- 页面链接: https://www.zingnex.cn/en/forum/thread/llama-factory-b06f67f9
- Canonical: https://www.zingnex.cn/forum/thread/llama-factory-b06f67f9
- Markdown 来源: floors_fallback

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## Introduction / Main Post: Practical Guide to Fine-Tuning Vision-Language Models Based on LLaMA-Factory: Document Understanding and Chart Parsing

This article introduces a fine-tuning project for Vision-Language Models (VLM) based on the LLaMA-Factory framework, focusing on document understanding, chart parsing, and visual question answering tasks. The project demonstrates how to use LoRA and full fine-tuning techniques to enhance the performance of VLMs in specific domains, and provides complete architecture design, training workflow, and performance evaluation results.